The most clinically fine-tuned AI models in healthcare documentation.
ClinixSummary is powered by proprietary large language models built exclusively for clinical documentation. Trained on hundreds of thousands of hours of de-identified clinical audio spanning 40+ specialties and allied health, these models do not guess — they understand.
Deep Clinical Fine-Tuning
Our models are not re-skinned general-purpose AI. They are purpose-built from the ground up on real clinical audio — hundreds of thousands of hours of de-identified recordings across every major medical, dental, behavioural health, veterinary, and allied health specialty.
40+ Specialties & Allied Health
From cardiology and dermatology to physiotherapy, occupational therapy, and midwifery — each specialty has its own domain-tuned module. A cardiology encounter is processed differently from a dermatology consult or a physiotherapy assessment because the clinical language, documentation patterns, and coding requirements are fundamentally different.
Trained on Real-World Audio
Our training data includes clinicians with accents, background noise from busy clinics, interruptions, and the natural cadence of real patient encounters. We capture pauses, self-corrections, shorthand abbreviations, and multilingual code-switching — the way clinicians actually speak, not how textbooks say they should.
Semantic Understanding, Not Keywords
This is not keyword matching or simple transcription. Our models parse clinical semantics — understanding that “the patient’s sugars have been running high” means uncontrolled hyperglycaemia, not a dietary preference. Every utterance is contextualised within the clinical encounter.
The effort behind the models: Building and maintaining 40+ specialty-specific modules requires continuous clinical collaboration, rigorous QA, and weekly refinement cycles. Every module is validated by domain experts before deployment and continuously improved through our Kai-zen feedback loop.
Contextual Inference & Clinical Reasoning
This is the key differentiator. Our models do not merely transcribe words — they infer clinical context from natural conversation and generate documentation that reflects clinical reasoning.
Implied Clinical Context
When a clinician says “the rash spread to the trunk after starting the new medication,” our model understands the implied drug reaction. It connects this observation to the medication history, flags a potential adverse drug event, and documents the temporal relationship — without the clinician having to spell it out.
- link Automatically connects symptoms to medication history
- diagnosis Extends to differential diagnosis suggestions
- receipt_long Recommends ICD-10 codes based on clinical narrative
Grounded in Captured Facts
Our models never fabricate clinical information. Every inference is grounded in what was actually said during the encounter. If the clinician did not mention it, the model does not invent it. This is a non-negotiable design principle that separates ClinixSummary from general-purpose AI tools that may hallucinate clinical details.
- shield Zero hallucination policy for clinical facts
- fact_check Every output traceable to source audio
- gavel Medico-legal defensibility by design
Why Proprietary Matters
Generic large language models were not built for medicine. They lack the domain depth, the privacy architecture, and the clinical rigour that healthcare documentation demands. Here is why proprietary models are essential.
Sub-Second Processing
Purpose-built inference pipelines deliver structured notes in under a second. No waiting, no lag — documentation keeps pace with the consultation.
Privacy by Architecture
Patient data never leaves our secure infrastructure. Unlike generic APIs, our models run on dedicated, HIPAA-compliant compute with zero data sharing.
Continuously Learning
Through our Kai-zen programme, models are updated weekly — incorporating clinician feedback, new terminology, and evolving clinical guidelines.
Multi-Specialty Depth
40+ specialty modules including allied health. Each module understands the unique terminology, documentation patterns, and coding requirements of its domain.
Allied Health Coverage
Clinical AI should not be limited to physicians. Our models explicitly cover allied health disciplines — each with its own documentation patterns, terminology, and outcome measures that differ fundamentally from medical specialties.
Physiotherapy
ROM assessments, functional outcome measures, exercise prescription documentation, and treatment progression notes with standardised physiotherapy terminology.
Occupational Therapy
ADL assessments, cognitive-perceptual evaluations, home modification recommendations, and goal-oriented treatment plans using OT-specific frameworks.
Speech & Language Therapy
Articulation assessments, fluency disorder documentation, swallowing evaluations, and language development tracking with SLT-specific scoring systems.
Midwifery
Antenatal visit documentation, birth plans, labour progress notes, and postnatal assessments following midwifery-specific documentation standards and continuity models.
Nutritional Therapy
Dietary assessments, nutritional risk screening, meal plan documentation, and micronutrient tracking with clinical nutrition terminology and evidence-based frameworks.
And More
Podiatry, audiology, optometry, clinical psychology, social work, and other allied health disciplines — each with domain-tuned modules reflecting their unique clinical language.
Experience the difference proprietary models make.
See why health systems, solo practitioners, and allied health professionals trust ClinixSummary’s purpose-built AI over generic alternatives.